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Record W2274456854

Application of Advanced Cementitious Materials in Infrastructure

2016· article· en· W2274456854 on OpenAlexaboutno aff
I. Shalom

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityPrecast concreteCementitiousSilica fumeEngineeringCivil engineeringForensic engineeringCementMaterials scienceWaste managementFly ashComposite material
DOInot available

Abstract

fetched live from OpenAlex

Advanced Cementitious Materials (ACM’s) are products with materials found in conventional concrete (cement, silica fume, sand, superplasticizer, and water) plus distinctive materials like fibers (steel, carbon) and quartz. The superiority of ACM’s in terms of strength, ductility, and durability marks it as high potential replacement of traditional concrete. The growing expansion of ACM applications and technical experience gained in the last two decades in counties including Japan, Germany, Austria, Australia, USA, Denmark, Canada, France, and the Netherlands results in new frontier of cement materials used in infrastructure. The economic feasibility of ACM’s has been demonstrated in footbridges, outstanding bridges, and large pre-cast series. Furthermore safety and durability of ACM’s, especially of Ultra-high performance concrete (UHPC) has been proven encouraging further research efforts. This research is one part of an ongoing research under the supervision of Professor D.A. Hordijk at the Technical University of Delft on the application of ACM’s in infrastructure. The biggest potential for new infrastructure with ACM’s is in precast girders and thin plates. Throughout utilizing the excellent properties of ACM’s like UHPC, a new lighter- weight, durable, efficient, and adaptable superstructure has been developed in this study to replace the existing traditional design of bridges in the nearby future. One example of the approach taken in this thesis is the benefit of long life-cycle. Due to dense matrix, which prevents the ingress of detrimental substances apply UHPC selectively in the superstructure where it required to sustain high level of durability. Another example of the mindset of this thesis is lightweight design, means material distribution follows the forces distribution. In this thesis the outcome of an extensive material study was an overview of the ACM’s properties, time depended behavior, non-linear behavior, design standards, and field of application. Based on the material study different parameters are analyzed to enhance the understanding of the behavior of structure with ACM. It has been found that structural elements from UHPC are far more efficient then their corresponded traditional concrete structures. The maximum crack size is significantly lower, the slenderness is much higher which result in higher efficiency and reduction of the dead loads. Also the shrinkage and creep of ACM’s is studied. The time-depended imposed deformation of UHPC elements (plate, flange, truss member) with different sizes cause stresses in the model ends up as transverse cracks when the deformation is restrained. Based on the drawn conclusion of the parametric study, the design stage of the new ACM superstructure was initiated complying with requirements & boundaries according to the NEN norms and SETRA (French recommendation for UHPC). The new superstructure is 35% lighter than traditional solution, with efficient material distribution, built only from concrete, with elegant simple solutions. The low reliability of the fibers as replacement for reinforcement well-thought-out in the design. Some suggestion for future research in the field of modular adaptable superstructure have promising potential as far as applying ACM’s.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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